In this paper, we described and developed a framework for Multilayer Perceptron (MLP) to work on low level image processing, where MLP will be used to perform image super-resolution. Meanwhile, MLP are trained with different types of images from various categories, hence analyse the behaviour and performance of the neural network. The tests are carried out using qualitative test, in which Mean ...

This paper presents an effective arrhythmia classification algorithm using the heart rate variability (HRV) signal. The proposed method is based on the Generalized Discriminant Analysis (GDA) feature reduction technique and the Multilayer Perceptron (MLP) neural network classifier. At first, nine linear and nonlinear features are extracted from the HRV signals and then these features are reduce...

Speaker recognition is the process of automatically recognizing who is speaking on the basis of individual information included in speech waves. Feature extraction for speech recognition is a subject of a major interest today; different features have been investigated in speech recognition systems. The perceptual linear predictive PLP: this technique uses three concepts from the psychophysics o...

Received Sep 17, 2017 Revised Nov 18, 2017 Accepted Nov 23, 2017 Computational time is high for Multilayer perceptron (MLP) trained with back propagation learning algorithm (BP) also the complexity of the network increases with the number of layers and number of nodes in layers. In contrast to MLP, functional link artificial neural network (FLANN) has less architectural complexity, easier to tr...

In this paper a novel the artificial neural networks are used for both residual generation and residual analysis for fault diagnosis of robust manipulators. A Multilayer Perception (MLP) is employed to reproduce the dynamics of the robotic manipulator. Its outputs are compared with actual position and velocity measurements, generating the so-called residual vector. The residuals, when properly ...

This paper describes the development of an Intellectual Property (IP) core in VHDL able to implement a Multilayer Perceptron (MLP) artificial neural network (ANN) topology with up to 2 hidden layers, 128 neurons, and 31 inputs per neuron. Neural network models are usually developed by using programming languages, such as Matlab®. However, their implementation in configurable logic hardware requ...

This paper compares four different artificial neural network approaches for computer network traffic forecast, such as: (1) Multilayer Perceptron (MLP) using the Backpropagation as training algorithm; (2) MLP with Resilient Backpropagation (Rprop); (3) Recurrent Neural Network (RNN); (4) deep learning Stacked Autoencoder (SAE). The computer network traffic is sampled from the traffic of the net...

This article describes linear and nonlinear Artificial Neural Network(ANN)-based predictors as Autoregressive Moving Average models with Auxiliary input (ARMAX) process for Signal to Interference plus Noise Ratio (SINR) prediction in Direct Sequence Code Division Multiple Access (DS/CDMA) systems. The Multi Layer Perceptron (MLP) neural network with nonlinear function is used as nonlinear neura...

The prediction and estimation of suspended sediment concentration are investigated by using multi-layer perceptrons (MLP). The fastest MLP training algorithm, that is the Levenberg-Marquardt algorithm, is used for optimization of the network weights for data from two stations on the Tongue River in Montana, USA. The first part of the study deals with prediction and estimation of upstream and do...